Value Chain Marketing: Multi-level Analysis Basics

[Editor’s note: This installment is part of an ongoing series. You canstart at the beginningin order to follow its logical sequence.]

by Kenneth Rudich

The task of effectively implementing a value chain strategy hinges on being able to identify — and link together — the various dimensions or parts of dimensions involved in delivering value fulfillment.

Each linkage is designed to transform the separate pieces or parts into something stronger and better — by having them work in harmony so the whole becomes greater than the sum of the parts. In business and marketing this is known as synergism, and as discussed earlier it arises from the successful convergence of a broadly integrated effort.

For our purposes, a linkage exists if the performance or cost of one activity affects that of another.

The intent of the generic value chain is to have a tool that helps to isolate the value-creating activities within a marketing strategy, then map them, link them, and track them with time.

Identifying the relevant linkages derives in part from knowing there are multiple levels of them. They evolve from the multiple levels of activity that exist within a dimension.

One noteworthy aspect of these multiple levels rests with the ability to smartly organize them, usually in a graduated format.

In this case, graduated means moving from the summary level of information about an activity to ever increasing detail or granularity at each successive lower level (Though there are exceptions where data cannot be structured like this, a significant and relevant amount can. The other type of data, known as unstructured data, will be discussed later.).

The lowest level is known as the atomic level or source level in the data warehousing world.

Let’s look more closely at the nature of this structure architecture with the help of the customer breakout graphic (shown above) as an example.

If we start at the atomic level and drill up, we have customers followed by collections of customers, followed by collections of collections of customers, followed by one ginormous collection of the collections.

As you can see, this upward progression leads to creating an increasingly abstract pool of customers, which is another way of saying they essentially become more and more faceless. This, in turn, makes it harder and harder to relate to them in a personally meaningful way.

But then the opposite occurs when we drill down. Suddenly, their characteristics snap into better focus, and it becomes easier to personally relate with them.

In marketing, this structural dynamic is significant because it gives the analyst an option to look at the same information from different angles or perspectives.

The important feature of this structure is that it preserves the vertical relationships commonly found within a dimension.

When we deploy data points to represent these structures, then add metrics or measures (also known as derived data or decision support analytics), all within the bigger context of the value chain model, it becomes especially useful for analysis purposes.

An analyst can pull apart a dimension and put it back together again to see how the different pieces and parts fit together to form the whole.

It also can be used to isolate significant patterns and trends.

There are several synonymous terms for interrogating the data in this manner.

Horizontal-Vertical Convergence

This broadens the analysis to include both horizontal and vertical views of the value chain, and it allows what is known in analytical circles as slice-and-dice. As the name suggests, it enables the technical capacity to dissect the data in a multidimensional manner.

So now we have a mechanism for monitoring and managing the rhythm and flow of what’s going on internally, in relation/response to what’s going on externally.

Let’s quickly look at two commonly seen examples.

Earlier, we said the external force of time has the dimensional properties of past, present and future. In addition to these horizontal properties, it also has vertical properties.

For instance, the past and the present can be broken down into vertical time increments such as months, weeks, days, hours and minutes so as to measure productivity gains/losses from one time frame to the other. This could entail questions like:

— have quality control rates changed during a given time frame (e.g., from last quarter to this quarter), how and by how much?

— have cost efficiencies changed, how and by how much?

— have time efficiencies within or across key processes changed, how and by how much (e.g., producing more in the same amount of time due to process flow improvements)?

In another instance, the past to present time interval can be used to flesh out the current trajectory of the enterprise in key areas like revenues and sales. These results can inform future planning, which might be vertically structured into a progressive sequence of forward looking steps, such as short term actions, intermediate term actions and long term vision.

Though these two examples clearly differ in purpose, they nonetheless are related within the context of the value chain, each no less important than the other.

Moreover, placing them together like this offers a good segue for moving onto the next topic, which delves further into the nature of their relationship.